Predicting Transcriptional and Epigenetic Networks in Cancer from Sequencing Data

项目描述（申请人提供）：本项目旨在开发生物信息学资源，用于处理和整合快速涌现的用于癌症研究中致癌转录因子（TFs）研究的大规模测序数据。虽然我们的方法将适用于一般tf，但我们将通过专注于小眼相关转录因子（MITF）来开发我们的工具，这是黑色素瘤中经常扩增的关键癌蛋白。MITF可能是黑色素瘤中研究最深入的TF，它负责将多种信号转化为增殖、存活和侵袭的转录控制。因此，研究MITF等致癌TF的作用机制，全面识别其直接靶基因，仍然是癌症研究中亟待解决的重要问题。基于高通量DNA测序的癌症基因组学现在正在迅速产生大量的遗传和表观遗传数据，这些数据可以共同揭示MITF如何作为黑色素瘤进展的有效调节剂发挥作用。分析如此庞大的异构数据集经常受到测序失败和缺乏整合和解释结果信息的分析方法的挑战。提出的工具将解决这些紧迫的问题：(1)我们将为ChIP-seq和RNA-seq数据开发一个独立的平台-独立的质量控制可视化软件。我们的软件包将自动测试和图形化总结数据的质量，并提示潜在的故障来源；(2)开发并应用计算工具来发现MITF的合作TFs。TF本身的结合活性通常不足以调节基因表达，这表明协同因子的特定组合至关重要地决定了MITF转录黑色素瘤中关键癌基因的能力。因此，我们将通过将ChIP-seq数据与DNA序列分析相结合，计算识别和实验验证MITF的协同因子；(3)我们将开发和应用统计方法来推断由MITF控制和指导的表观遗传变化，从而识别破坏正常MITF功能的异常表观遗传修饰；(4)由于非编码rna （ncRNAs）和反转录转座子的异常表达可以严重改变细胞周期、凋亡和增殖，我们将在黑色素瘤中鉴定活性ncRNAs和反转录转座子，并发现它们的转录调控因子。这些结果将有助于揭示黑色素瘤中MITF的转录和表观遗传网络，并为应用于其他癌症提供宝贵的资源。

DESCRIPTION (provided by applicant): This project aims to develop bioinformatic resources for processing and integrating the large-scale sequencing data that are rapidly emerging for studying oncogenic transcription factors (TFs) in cancer research. While our methods will be applicable to general TFs, we will develop our tools by focusing on microphthalmia-associated transcription factor (MITF), a key onco-protein frequently amplified in melanoma. MITF is perhaps the most intensely studied TF in melanoma, being responsible for turning multiple signals into a transcriptional control of proliferation, survival, and invasion. Studying the mechanisms of an oncogenic TF, such as MITF, and comprehensively identifying its direct target genes thus remain important unsolved problems in cancer research. Cancer genomics based on high-throughput DNA sequencing is now rapidly generating enormous amounts of genetic and epigenetic data that can collectively reveal how MITF functions as a potent regulator of melanoma progression. Analyzing such massive heterogeneous datasets is frequently challenged by both sequencing failures and the lack of analysis methods for integrating and interpreting the resulting information. The proposed tools will address these urgent problems: (1) We will develop a stand-alone platform- independent quality control visualization software for ChIP-seq and RNA-seq data. Our software package will automatically test and graphically summarize the quality of data and also suggest potential sources of failure; (2) We will develop and apply computational tools for discovering cooperating TFs of MITF. TF binding activity in itself is often insufficient to regulate gene expression, suggesting that specific combinations of cooperating factors crucially determine MITF's ability to transcribe key oncogenes in melanoma. We will thus computationally identify and experimentally validate cooperating factors of MITF by combining ChIP-seq data with DNA sequence analysis; (3) We will develop and apply statistical methods for inferring the epigenetic changes that are both controlled by and guiding MITF and, as a result, identify aberrant epigenetic modifications that disrupt normal MITF functions; (4) As aberrant expression of non-coding RNAs (ncRNAs) and retrotransposons can critically alter cell cycle, apoptosis and proliferation, we will identify active ncRNAs and retrotransposons in melanoma and discover their transcriptional regulators. These results will help reveal the transcriptional and epigenetic network of MITF in melanoma and produce valuable resources applicable to other cancers.